As mobile Internet technologies develop and the use of mobile terminals prevail, many news-recommending systems spring up in the market to enable a user to more conveniently browse various latest news resources in time through a mobile terminal. The news-recommending system may learn about the user's interest according to the user's self-portrait, and thereby recommend articles of the user's interest to the user purposefully according to the user's interest. However, in the prior art, articles included in the news-recommending system are of different quality, they might include some authoritative high-quality articles, or some crude and fake news, or some low-quality articles which are intended to attract the user's attention and contravene facts. Therefore, it is necessary to check quality of news content in the news-recommending systems.
In the current news-recommending systems, there often occurs a phenomenon that news resources of lower quality are often plagiarized mutually and appear repeatedly. For example, a piece of news that already proves fake news might be slightly modified by some ill-intended news authors and issued repeatedly to attract readers' attention and cheat readers in clicking. At present, some problems about repeated appearance of low-quality news resources with similar content are mainly solved by manually checking many times, with a very low efficiency.
However, low-quality news resources appearing repeatedly by plagiarism in the news-recommending systems are recognized in a manually-checking manner in the prior art. However, regarding a news-recommending system with a great deal of news increased every day, the manually-checking manner is time-consuming and arduous, might be confronted with miss in check, and causes a very low recognition efficiency of low-quality news resources.